Mini-Project | Sem 3

Russia-Ukraine War

Mini-Project | Sem 3

Russia Ukraine War

    

Aadith Sukumar | AIML A1 | 21070126003

Russia War Losses Dataset

2022 Russian Invasion of Ukraine
On 24 February 2022, Russia invaded Ukraine. The event started after a long military crisis. Russia supported the Donetsk People's Republic and the Luhansk People's Republic in the days before the invasion.
Although this sudden and brutal invasion has only started in 2022, the war was going on between the two former Soviet colonies for more than half a decade.
The Russo-Ukrainian War, also known as the Russia–Ukraine War, has been ongoing between Russia (alongside Russian separatists in Ukraine) and Ukraine since February 2014.

Contents

1. Cover
2. Contents
3. Importing libraries
4. Loading Dataset
   • Extracting Data
   • Reading dataset into dataframe
5. Learning about dataset
   • Equipment losses
   • Personnel losses
6. Finding NULL Values
   • Equipment losses)
   • Personnel losses)
7. Data cleaning
8. Military Personnel lost
   • Future Prediction
9. Military Equipment lost
10. Extracting location of losses
11. Plotting on Map
12. Conclusion

Importing required libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as mn
from sklearn.impute import KNNImputer
# For plotting
import plotly
import plotly.graph_objs as go
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.io as pio
# Took to map in notebook
import geopandas as gpd
from geopandas.tools import geocode
import folium
from scipy.stats import linregress
from branca.element import Figure
import zipfile
import datetime as dt
# For prediction and ML
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

Loading DataSet

Extracting required dataset from Kaggle using API

We will need to download the dataset from Kaggle now. Kaggle user piterfm's dataset "2022 Ukraine Russia War" is the perfect fit. It is a dataset which is updated on a daily basis. Hence, instead of downloading it from the website each time, we shall extract it so we can run it as a command to ensure our analysis is always running on the latest and updated dataset.

!kaggle datasets download -d "piterfm/2022-ukraine-russian-war"

Downloading 2022-ukraine-russian-war.zip to C:\Users\Aadith Sukumar\Desktop\E-Learn\SIT\Second Year\Sem 3\Project\Russia-Ukraine War

  0%|          | 0.00/9.05k [00:00<?, ?B/s]
100%|##########| 9.05k/9.05k [00:00<00:00, 4.10MB/s]

# Unzipping the dataset downloaded
zipfile.ZipFile('2022-ukraine-russian-war.zip').extractall()

Reading the dataset as a Pandas Dataframe

equip_losses=pd.read_csv("./russia_losses_equipment.csv")
person_losses=pd.read_csv("./russia_losses_personnel.csv")

equip_losses.head()

	date	day	aircraft	helicopter	tank	APC	field artillery	MRL	military auto	fuel tank	drone	naval ship	anti-aircraft warfare	special equipment	mobile SRBM system	greatest losses direction	vehicles and fuel tanks	cruise missiles
0	2022-02-25	2	10	7	80	516	49	4	100.0	60.0	0	2	0	NaN	NaN	NaN	NaN	NaN
1	2022-02-26	3	27	26	146	706	49	4	130.0	60.0	2	2	0	NaN	NaN	NaN	NaN	NaN
2	2022-02-27	4	27	26	150	706	50	4	130.0	60.0	2	2	0	NaN	NaN	NaN	NaN	NaN
3	2022-02-28	5	29	29	150	816	74	21	291.0	60.0	3	2	5	NaN	NaN	NaN	NaN	NaN
4	2022-03-01	6	29	29	198	846	77	24	305.0	60.0	3	2	7	NaN	NaN	NaN	NaN	NaN

person_losses.head()

	date	day	personnel	personnel*	POW
0	2022-02-25	2	2800	about	0.0
1	2022-02-26	3	4300	about	0.0
2	2022-02-27	4	4500	about	0.0
3	2022-02-28	5	5300	about	0.0
4	2022-03-01	6	5710	about	200.0

Learning more about the dataset

Equipment losses

equip_losses.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 257 entries, 0 to 256
Data columns (total 18 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   date                       257 non-null    object 
 1   day                        257 non-null    int64  
 2   aircraft                   257 non-null    int64  
 3   helicopter                 257 non-null    int64  
 4   tank                       257 non-null    int64  
 5   APC                        257 non-null    int64  
 6   field artillery            257 non-null    int64  
 7   MRL                        257 non-null    int64  
 8   military auto              65 non-null     float64
 9   fuel tank                  65 non-null     float64
 10  drone                      257 non-null    int64  
 11  naval ship                 257 non-null    int64  
 12  anti-aircraft warfare      257 non-null    int64  
 13  special equipment          238 non-null    float64
 14  mobile SRBM system         36 non-null     float64
 15  greatest losses direction  181 non-null    object 
 16  vehicles and fuel tanks    192 non-null    float64
 17  cruise missiles            192 non-null    float64
dtypes: float64(6), int64(10), object(2)
memory usage: 36.3+ KB

equip_losses.describe()

	day	aircraft	helicopter	tank	APC	field artillery	MRL	military auto	fuel tank	drone	naval ship	anti-aircraft warfare	special equipment	mobile SRBM system	vehicles and fuel tanks	cruise missiles
count	257.000000	257.000000	257.000000	257.000000	257.000000	257.00000	257.000000	65.000000	65.000000	257.000000	257.000000	257.000000	238.000000	36.000000	192.000000	192.000000
mean	130.000000	202.540856	177.696498	1533.268482	3491.813230	836.18677	226.782101	1047.507692	69.323077	616.863813	12.140078	111.599222	76.760504	3.944444	3022.583333	193.989583
std	74.333707	61.332464	49.397792	706.934421	1313.876455	467.34927	97.472909	466.162060	7.545917	405.187265	4.297054	51.071095	42.980191	0.333333	674.695195	80.852072
min	2.000000	10.000000	7.000000	80.000000	516.000000	49.00000	4.000000	100.000000	60.000000	0.000000	2.000000	0.000000	10.000000	2.000000	1796.000000	84.000000
25%	66.000000	190.000000	155.000000	1008.000000	2445.000000	436.00000	151.000000	600.000000	60.000000	232.000000	8.000000	77.000000	42.000000	4.000000	2508.750000	129.000000
50%	130.000000	217.000000	187.000000	1584.000000	3744.000000	801.00000	246.000000	1178.000000	73.000000	654.000000	15.000000	105.000000	66.500000	4.000000	2962.000000	182.000000
75%	194.000000	236.000000	206.000000	2068.000000	4459.000000	1157.00000	294.000000	1437.000000	76.000000	867.000000	15.000000	156.000000	115.500000	4.000000	3615.000000	239.250000
max	258.000000	278.000000	260.000000	2786.000000	5654.000000	1791.00000	391.000000	1701.000000	76.000000	1476.000000	16.000000	203.000000	159.000000	4.000000	4216.000000	399.000000

print("Size =",equip_losses.size)
print("Shape =",equip_losses.shape)

Size = 4626
Shape = (257, 18)

Personnel losses

person_losses.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 257 entries, 0 to 256
Data columns (total 5 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   date        257 non-null    object 
 1   day         257 non-null    int64  
 2   personnel   257 non-null    int64  
 3   personnel*  257 non-null    object 
 4   POW         62 non-null     float64
dtypes: float64(1), int64(2), object(2)
memory usage: 10.2+ KB

person_losses.describe()

	day	personnel	POW
count	257.000000	257.000000	62.000000
mean	130.000000	37564.225681	386.387097
std	74.333707	17782.889253	131.440363
min	2.000000	2800.000000	0.000000
25%	66.000000	23200.000000	389.000000
50%	130.000000	35970.000000	421.000000
75%	194.000000	49800.000000	474.500000
max	258.000000	77170.000000	496.000000

print("Size =",person_losses.size)
print("Shape =",person_losses.shape)

Size = 1285
Shape = (257, 5)

Finding NULL Values in DataSet

Equipment losses (null)

mn.matrix(equip_losses, figsize=(20,5))

<AxesSubplot:>

equip_null = pd.DataFrame((equip_losses.isnull().sum())*100/equip_losses.shape[0]).reset_index()
equip_null.columns = ['Column Name', 'Null Values Percentage']
fig = plt.figure(figsize=(10,5))
ax = sns.pointplot(x="Column Name",y="Null Values Percentage",data=equip_null,color='blue')
plt.xticks(rotation =90,fontsize =7)
ax.axhline(50, ls='--',color='red')
plt.title("Percentage of NULL values in equipment losses data")
plt.ylabel("Null Values (%)")
plt.xlabel("Columns", )
plt.show()

fig,ax = plt.subplots(figsize=(15, 3))
equip_null=equip_null.set_index(equip_null['Column Name'])
sns.heatmap(pd.DataFrame(equip_null["Null Values Percentage"]).transpose(),cmap = 'coolwarm')
plt.show()

Insight

We can notice that 'mobile SRBM system' column has the most null values, followed by 'military auto' and 'fuel tank' columns.

Personnel losses (null)

mn.matrix(person_losses, figsize=(15,2))

<AxesSubplot:>

person_null = pd.DataFrame((person_losses.isnull().sum())*100/person_losses.shape[0]).reset_index()
person_null.columns = ['Column Name', 'Null Values Percentage']
fig = plt.figure(figsize=(10,5))
ax = sns.pointplot(x="Column Name",y="Null Values Percentage",data=person_null,color='blue')
plt.xticks(rotation =90,fontsize =7)
ax.axhline(50, ls='--',color='red')
plt.title("Percentage of NULL values in personnel losses data")
plt.ylabel("Null Values (%)")
plt.xlabel("Columns", )
plt.show()

Insight

We can notice that 'POW' column has the most null values.

Data cleaning

equip_null = pd.DataFrame((equip_losses.isnull().sum())*100/equip_losses.shape[0]).reset_index()
equip_null.columns = ['Column Name', 'Null Values Percentage']
equip_null

	Column Name	Null Values Percentage
0	date	0.000000
1	day	0.000000
2	aircraft	0.000000
3	helicopter	0.000000
4	tank	0.000000
5	APC	0.000000
6	field artillery	0.000000
7	MRL	0.000000
8	military auto	74.708171
9	fuel tank	74.708171
10	drone	0.000000
11	naval ship	0.000000
12	anti-aircraft warfare	0.000000
13	special equipment	7.392996
14	mobile SRBM system	85.992218
15	greatest losses direction	29.571984
16	vehicles and fuel tanks	25.291829
17	cruise missiles	25.291829

equip_losses.isnull().sum()

date                           0
day                            0
aircraft                       0
helicopter                     0
tank                           0
APC                            0
field artillery                0
MRL                            0
military auto                192
fuel tank                    192
drone                          0
naval ship                     0
anti-aircraft warfare          0
special equipment             19
mobile SRBM system           221
greatest losses direction     76
vehicles and fuel tanks       65
cruise missiles               65
dtype: int64

# Dropping columns "mobile SRBM system" and "military auto", "fuel tank" from equipment dataset

equip_losses = equip_losses.drop(['mobile SRBM system','military auto','fuel tank'],axis=1)
person_losses = person_losses.drop(['personnel*'],axis=1)

special_equipment = equip_losses['special equipment']
equip_losses.info()
equip_null

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 257 entries, 0 to 256
Data columns (total 15 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   date                       257 non-null    object 
 1   day                        257 non-null    int64  
 2   aircraft                   257 non-null    int64  
 3   helicopter                 257 non-null    int64  
 4   tank                       257 non-null    int64  
 5   APC                        257 non-null    int64  
 6   field artillery            257 non-null    int64  
 7   MRL                        257 non-null    int64  
 8   drone                      257 non-null    int64  
 9   naval ship                 257 non-null    int64  
 10  anti-aircraft warfare      257 non-null    int64  
 11  special equipment          238 non-null    float64
 12  greatest losses direction  181 non-null    object 
 13  vehicles and fuel tanks    192 non-null    float64
 14  cruise missiles            192 non-null    float64
dtypes: float64(3), int64(10), object(2)
memory usage: 30.2+ KB

	Column Name	Null Values Percentage
0	date	0.000000
1	day	0.000000
2	aircraft	0.000000
3	helicopter	0.000000
4	tank	0.000000
5	APC	0.000000
6	field artillery	0.000000
7	MRL	0.000000
8	military auto	74.708171
9	fuel tank	74.708171
10	drone	0.000000
11	naval ship	0.000000
12	anti-aircraft warfare	0.000000
13	special equipment	7.392996
14	mobile SRBM system	85.992218
15	greatest losses direction	29.571984
16	vehicles and fuel tanks	25.291829
17	cruise missiles	25.291829

Military Personnel lost by Russia in the War

x, y = person_losses['date'], person_losses['personnel']
# Base of figure
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, mode='markers',line_color="red"))
# Adding additional details
fig.update_layout(title="Russian Personnel Losses", xaxis_title="Date", yaxis_title="Count", margin=dict(l=0, r=0, t=50, b=50))
fig.show()

Future Prediction

# Adding a time refernce list to match the size of dataframe
pred_list=[]
for i in range(person_losses.shape[0]):
    pred_list.append(i)
pred_list

# Adding to dataframe
person_losses["time_ref"]=pred_list
person_losses

	date	day	personnel	POW	time_ref
0	2022-02-25	2	2800	0.0	0
1	2022-02-26	3	4300	0.0	1
2	2022-02-27	4	4500	0.0	2
3	2022-02-28	5	5300	0.0	3
4	2022-03-01	6	5710	200.0	4
...	...	...	...	...	...
252	2022-11-04	254	74840	NaN	252
253	2022-11-05	255	75440	NaN	253
254	2022-11-06	256	75930	NaN	254
255	2022-11-07	257	76460	NaN	255
256	2022-11-08	258	77170	NaN	256

257 rows × 5 columns

# Predicting the number of personnel losses for the next 5 years

X = person_losses['time_ref'].values.reshape(-1,1)
y = person_losses['personnel'].values.reshape(-1,1)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

regressor = LinearRegression()
regressor.fit(X_train, y_train)

y_pred = regressor.predict(X_test)

# Adding a longer range of time refernce to extend prediction into the future
pred_list=[]
for i in range(305):
    pred_list.append(i)

pred_list = np.array(pred_list).reshape(-1,1)
pred = regressor.predict(pred_list)

# Plotting both the actual and predicted values on the same graph
fig = go.Figure()
fig.add_trace(go.Scatter(x=pred_list.reshape(-1), y=pred.reshape(-1), mode='lines',line_color="red",name="Predicted"))
fig.add_trace(go.Scatter(x=X_test.reshape(-1), y=y_test.reshape(-1), mode='markers',line_color="blue",name="Actual"))
fig.update_layout(title="Predicted vs Actual Russian Personnel Losses", legend=dict(x=0, y=1), xaxis_title="Date", yaxis_title="Count", margin=dict(l=0, r=0, t=50, b=50))
fig.show()

# Calculating the accuracy of model
print("Accuracy of the model =",round(regressor.score(X_test,y_test)*100,4),"%")

Accuracy of the model = 96.631 %

Important:

Since the war is presently still ongoing at the time of analysis, the data further might change drastically depending on further attacks from any belligerents. The accuracy and thus produced predictive results might not back a sudden intense warfare where losses might exponentially rise or of a subsiding war in the near future.

Military Equipment lost by Russia in the War

x = equip_losses['date']
y0 = equip_losses['aircraft']
y1 = equip_losses['helicopter']
y2 = equip_losses['anti-aircraft warfare']
y3 = equip_losses['drone']
y4 = equip_losses['tank']
y5 = equip_losses['APC']
y6 = equip_losses['naval ship']
y7 = equip_losses['field artillery']
fig = go.Figure()

fig.add_trace(go.Scatter(x=x, y=y0, mode='lines+markers', name='Aircraft'))
fig.add_trace(go.Scatter(x=x, y=y1, mode='lines+markers', name='Helicopter'))
fig.add_trace(go.Scatter(x=x, y=y2, mode='lines+markers', name='Anti-aircraft warfare'))
fig.add_trace(go.Scatter(x=x, y=y3, mode='lines+markers', name='Drone'))
fig.add_trace(go.Scatter(x=x, y=y4, mode='lines+markers', name='Tank'))
fig.add_trace(go.Scatter(x=x, y=y5, mode='lines+markers', name='APC'))
fig.add_trace(go.Scatter(x=x, y=y6, mode='lines+markers', name='Naval Ship'))
fig.add_trace(go.Scatter(x=x, y=y7, mode='lines+markers', name='Field Artillery'))

fig.update_layout(legend_orientation="h", legend=dict(x=0, y=1), title="Russian Equipment Losses", xaxis_title="Date", yaxis_title="Count", margin=dict(l=0, r=0, t=30, b=0))
fig.show()

Extracting location of losses

location_loss = equip_losses['greatest losses direction']
location_loss

0                              NaN
1                              NaN
2                              NaN
3                              NaN
4                              NaN
                  ...             
252             Lyman and Avdiivka
253             Lyman and Avdiivka
254             Lyman and Avdiivka
255    Lyman, Bakhmut and Avdiivka
256           Bakhmut and Avdiivka
Name: greatest losses direction, Length: 257, dtype: object

# Splitting the names according to duplicacy
location_loss = location_loss.dropna()

location_list = location_loss[(~location_loss.str.contains('and|,'))].unique().tolist()
location_list

['Sievierodonetsk',
 'Izyum',
 'Popasna',
 'Slobozhanskyi',
 'Novopavlivsk',
 'Avdiivka',
 'Kurakhove',
 'Bakhmut',
 'Lyman',
 'Zaporizhzhia',
 'Sloviansk',
 'Kramatorsk',
 'Mykolaiv',
 'Kryvyi Rih',
 'Donetsk']

# Filtering out data into a new dataframe

equip_losses2 = equip_losses[['date','greatest losses direction']]
equip_losses2 = equip_losses2.dropna()
equip_losses2['date'] = pd.to_datetime(equip_losses2['date'])
equip_losses2.replace(" and",",",regex=True, inplace=True) 

# Converting object string into a list
equip_losses2['listlosses'] = "[1,2,3]"
j=0
for i in equip_losses2['greatest losses direction']:
    list_losses=[]
    list_losses.append(i)
    equip_losses2.iloc[j,2] = list_losses
    j+=1

equip_losses2.drop('greatest losses direction', axis=1, inplace=True)
equip_losses2['listlosses'] = equip_losses2['listlosses'].astype("string")

# equip_losses2.explode('listlosses')
equip_losses2.head(20)

	date	listlosses
59	2022-04-25	['Sievierodonetsk']
60	2022-04-26	['Sievierodonetsk']
61	2022-04-27	['Kurakhove, Izyum']
62	2022-04-28	['Zaporizhzhia, Izyum']
63	2022-04-29	['Izyum']
64	2022-04-30	['Izyum']
65	2022-05-01	['Izyum']
66	2022-05-02	['Izyum']
67	2022-05-03	['Izyum']
68	2022-05-04	['Izyum, Novopavlivsk']
69	2022-05-05	['Popasna']
70	2022-05-06	['Lyman, Kurakhove']
71	2022-05-07	['Slobozhanskyi']
72	2022-05-08	['Novopavlivsk']
73	2022-05-09	['Avdiivka']
74	2022-05-10	['Avdiivka']
75	2022-05-11	['Novopavlivsk, Kurakhove, Sievierodonetsk']
76	2022-05-12	['Kurakhove']
77	2022-05-13	['Kurakhove']
78	2022-05-14	['Kurakhove']

# Adding location counts
loc_city = []
loc_count = []
for i in location_list:
    loc_city.append(i)
    loc_count.append(location_loss[location_loss.str.contains(str(i))].count())

loc_uk = pd.DataFrame(loc_city, columns =['City'])
loc_uk['Count'] = loc_count
loc_uk = loc_uk.sort_values('Count',ascending=False)
loc_uk

	City	Count
7	Bakhmut	63
14	Donetsk	62
11	Kramatorsk	32
13	Kryvyi Rih	25
5	Avdiivka	22
6	Kurakhove	14
10	Sloviansk	12
8	Lyman	10
9	Zaporizhzhia	10
1	Izyum	8
0	Sievierodonetsk	7
12	Mykolaiv	4
4	Novopavlivsk	3
2	Popasna	1
3	Slobozhanskyi	1

# Adding Co-ordinates for mapping

latitude = [48.002777,
48.59441,
48.738968,
47.910483,
47.9833,
48.136596,
48.8667,
47.837800,
49.209999,
48.94831944,
46.975033,
55.5333,
46.679443,
48.628242,
50.18932]
longitude = [37.805279,
37.99983,
37.584351,
33.391783,
37.2667,
37.7491335,
37.6167,
35.138300,
37.260746,
38.49166111,
31.994583,
28.65,
29.974368,
38.372715,
36.42414]
loc_uk['Latitude']=latitude
loc_uk['Longitude']=longitude
loc_uk

	City	Count	Latitude	Longitude
7	Bakhmut	63	48.002777	37.805279
14	Donetsk	62	48.594410	37.999830
11	Kramatorsk	32	48.738968	37.584351
13	Kryvyi Rih	25	47.910483	33.391783
5	Avdiivka	22	47.983300	37.266700
6	Kurakhove	14	48.136596	37.749133
10	Sloviansk	12	48.866700	37.616700
8	Lyman	10	47.837800	35.138300
9	Zaporizhzhia	10	49.209999	37.260746
1	Izyum	8	48.948319	38.491661
0	Sievierodonetsk	7	46.975033	31.994583
12	Mykolaiv	4	55.533300	28.650000
4	Novopavlivsk	3	46.679443	29.974368
2	Popasna	1	48.628242	38.372715
3	Slobozhanskyi	1	50.189320	36.424140

Plotting on Map

# Importing map from folio
fig=Figure(height=550,width=850)
uk_map = folium.Map(location=[48.5, 32.5], zoom_start=5.5, tiles='OpenStreetMap', max_zoom=7, min_zoom=4.5)

for x,y,z,w in zip(loc_uk.Latitude, loc_uk.Longitude, loc_uk.City, loc_uk.Count):
    #print(x,y)
    folium.Marker(location=[x,y],popup=w,tooltip=z,icon=folium.Icon(color='white',icon='bomb',prefix='fa',icon_color='black')).add_to(uk_map)

fig.add_child(uk_map)
uk_map

Conclusion

From the Analysis, We can say that most Losses take place in Donetsk city of Ukraine. Ukraine has lost control of 20 percent of its territory to Russian forces and their proxies in recent years. The destruction has already cost Ukraine at least 113.5 billion. If the war does not stop, then the equipment, military and civilian loss on both sides will be monumentally high.

“The war is a big disaster, and this disaster has a high price. With every meaning of this word. People lose money, reputation, quality of life, they lose freedom. But the main thing is that people lose their loved ones, they lose themselves.”
- Volodymyr Zelensky, President of Ukraine

The End.

Hopefully the war will too